Claude API vs OpenAI API Pricing Comparison (2026): Flagship and Mid-tier Costs Compared
Every price in this article comes from the two official pricing pages (Claude pricing documentation, OpenAI API pricing), as of 2026-10-15. This compares pricing only, not capability; prices and model lifecycles change, so check the official pages for current rates before budgeting.
"Which is cheaper, the Claude API or the OpenAI API?" is one of the most common selection questions, but one vendor names models by tier while the other names them by generation, so lining up the unit price tables is confusing. This article aligns both official price lists to the same day (as of 2026-10-15), compares unit prices across flagship, mid-tier, and lightweight tiers, and then explains the two billing details that most often widen a bill: caching and long context. If you want the complete Claude-only picture (including cache multipliers and cost-saving paths), read our Anthropic API pricing guide; this article focuses on the comparison.
1. Comparison basis: how to compare fairly
Three ground rules first. First, all prices are USD per million tokens (MTok) at official standard-tier list prices, excluding vendor batch discounts and regional endpoint surcharges (OpenAI Batch is roughly 50% of standard price, and Flex is priced the same as Batch — we use that in the selection section at the end). Second, we compare price, not capability: the two vendors' generation and naming cadences differ, and models at the same price may be positioned differently, so combine benchmarks with your own task tests. Third, both vendors keep adjusting prices and retiring models; the numbers here are a snapshot, not a long-term commitment.
2. Flagship tier comparison
Model | Input /MTok | Output /MTok |
|---|---|---|
Claude Opus 5.5 | $4 | $20 |
Claude Opus 5 | $5 | $25 |
OpenAI gpt-6-astra | $10 | $50 |
OpenAI gpt-5.5-pro | $30 | $180 |
The flagship gap is clear: Claude's flagship Opus 5.5 lists at $4/$20, while OpenAI's gpt-6-astra lists at $10/$50 — roughly 40% of the input price and 40% of the output price. OpenAI's pro line (gpt-5.5-pro, $30/$180) is a separate, more expensive product line aimed at high reasoning intensity. If your standard is "the flagship tier is good enough", the unit price table puts less billing pressure on Claude Opus 5.5; check actual capability differences with your own tests.
3. Mid-tier and lightweight comparison
Tier | Claude | OpenAI | Input / Output ($/MTok) |
|---|---|---|---|
Mid-tier | Sonnet 5.5 | gpt-6.1-sol | both $2 / $10 |
Mid-tier stretch | Sonnet 5.5 | gpt-5.6-sol | $2/$10 vs $4/$20 |
Lightweight | Haiku 5.5 (≤100K prompt) | gpt-6-luna | both $0.10 / $0.50 |
In the mid-tier and lightweight tiers the list prices have converged: $2/$10 for mid-tier and $0.10/$0.50 for lightweight are available on both sides. The real difference hides in the conditions:
Haiku 5.5 tiered pricing: once the prompt exceeds 100K tokens, input jumps to $0.50 and output to $2.50 (5× / 5×); gpt-6-luna's very-long-context tier is $0.20/$0.75. For lightweight models running long documents, this one item can reverse the price gap.
Context window surcharges: OpenAI draws the line at 272K, above which the long-context tier applies (e.g. gpt-6.1-sol becomes $4/$0.20/$15); Claude's official documentation states that models from 4.6 onward (except Haiku 5.5) bill the 1M-token context window at standard rates, with no long-context surcharge. This is the most concrete source of price difference on the Claude side for long documents and large codebases.
4. Cache billing: the key item for high-reuse workloads
Both vendors support prompt caching, with slightly different rules:
Claude: cache writes cost 1.25× the base input price (5-minute TTL) or 2× (1-hour TTL); cache reads cost 0.1×, and for Opus 5.5 / Sonnet 5.5 it is 0.05×. For Sonnet 5.5, a cache read is about $0.10/MTok.
OpenAI: cached input is about 10% of the base input price (gpt-6.1-sol: $0.10/MTok), and cache writes are billed separately (e.g. gpt-6.1-sol short context: $2.50/MTok).
For multi-turn conversations, fixed system prompts, and RAG prefix reuse, the cache hit rate drives the bill. Cache read prices are in the same order of magnitude on both sides (around $0.10/MTok); the differences are more in write multipliers and which tiers qualify. The longer the fixed prefix and the more it is reused, the more you should model caching as its own line item.
5. Two worked examples: turning unit prices into a monthly bill
Unit prices are rates per million tokens; the real size comes from multiplying by usage. Both examples below use the official snapshot prices cited above, with assumptions stated so you can recompute with your own usage.
Example A: everyday agent usage. Assume 30 million input tokens per month (80% of them cache reads) and 5 million output tokens. Flagship comparison: Claude Opus 5.5 input bill = 6M × $4 + 24M × $0.20 (0.05× cache read) = $28.8, output = 5M × $20 = $100, about $128.8 total; OpenAI gpt-6-astra input = 6M × $10 + 24M × $1 (cached price) = $84, output = 5M × $50 = $250, about $334 total. At the same usage the flagship tier shows roughly a 2.6× gap, driven mainly by unit price and the cache read multiplier.
Example B: long-document batch processing. Assume 100 requests per month, each averaging 200K input tokens and 5K output, with no caching. Claude Sonnet 5.5: input 20M × $2 = $40, output 0.5M × $10 = $5, about $45 total (1M context with no surcharge); Haiku 5.5 crosses the 100K prompt line and moves to the higher tier: 20M × $0.50 = $10, output 0.5M × $2.50 = $1.25, about $11.25 total. On the OpenAI side gpt-6.1-sol crosses 272K into the long-context tier: 20M × $4 = $80, output 0.5M × $15 = $7.5, about $87.5 total; gpt-6-luna's long-document tier is about $4.4. For the same "lightweight model on long documents" task, different tiering rules can produce a result an order of magnitude apart.
The shared conclusion of both examples: once the tier is right, what really moves the bill is the conditional items — cache hits, which context-length band you fall into, and whether you use a batch channel. The ratios in these examples are illustrative only; recompute with your actual usage.
6. Selection in three lines
Long context first (whole books, large repositories, very long sessions): prioritize Claude 4.6+'s 1M standard-price rule; on the lightweight side, watch Haiku 5.5's 100K tier break.
High-frequency cache reuse first (multi-turn agents with a fixed prefix): cache read prices are close on both sides; compute the 5%–10% read price and the write multiplier for your specific tier.
Offline batch jobs: OpenAI Batch/Flex at roughly 50% off is a clear feature (gpt-6/5.6-family Flex is priced the same as Batch); latency-insensitive batch runs are worth routing through that channel.
If you run heavy workloads on Claude Code or an agent framework, see the model tiering practices in our Claude Code getting-started tutorial for how tiers and billing rules map to a real bill.
7. Need both? One entry point, one bill
In practice few people use only one vendor: coding with Claude and ecosystem tools with OpenAI is a common combination. Registering two official APIs separately means two accounts, two overseas payment methods, and two bills. If you do not want to bind a foreign card twice, you can get one key directly at [CodeGateway](https://www.codegateway.dev/pricing): sign up and go, with both OpenAI- and Anthropic-compatible endpoints, and one billing view across both vendors' usage. Check the model catalog for available models, then run one real request through each endpoint on a small budget, compare output quality, and allocate volume accordingly.
Price sources: [Claude pricing documentation](https://platform.claude.com/docs/en/docs/about-claude/pricing) and [OpenAI API pricing](https://developers.openai.com/api/docs/pricing), as of 2026-10-15. This is a price snapshot comparison and constitutes no price commitment; official price changes govern. Model capability differences are outside this article's scope.